Continuous monitoring of respiratory rate or other biological signals can provide useful information for managing a patient's condition. For example, shortness of breath and difficulty in breathing are directly associated with deteriorating conditions in patients with heart failure (HF) and/or chronic obstructive pulmonary disease (COPD). The continuous monitoring of respiratory status in these patients can alert caregivers to administer early interventions to manage disease symptoms, which can prevent catastrophic events and improve quality of life.
However, continuous monitoring of respiratory rate or pattern is often not used in clinical practice due to the difficulty in performing these measurements, especially in non-intubated ambulatory settings. In particular, conventional measurement methods and systems that obtain data directly from the patient's airway are more accurate, but difficult to administer and often intolerable for patients. Conventional measurement methods and systems that rely on capturing chest motion often suffer from poor accuracy due to motion artifacts, thus making the measurements unsatisfactory for monitoring. For example, respiratory rate monitoring or monitoring of any biological signals requires the extraction of information from signals occurring within noise and attributing the information to the related biological event. These biological signals are often correlated and carry temporal and spatial information. The temporal signal may be obtained with conventional data acquisition systems (e.g., patient monitoring systems), but the spatial information requires enhanced digital signal processing when using the conventional systems. This digital signal processing requires additional processing resources including additional digital hardware and power to obtain or extract the spatial information. For example, redundant signals are needed for processing in conventional systems using principal component analysis (PCA) and independent component analysis (ICA) methods.